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Towards Rich Sensor Data Representation - Functional Data Analysis Framework for Opportunistic Mobile Monitoring

Topics: Ecological and Environmental Management; Geospatial Architectures and Middleware; Geospatial Information and Technologies ; Natural Phenomena Data Acquisition; Spatial Modeling and Reasoning; Spatio-temporal Data Acquisition; Spatio-Temporal Database Management

Authors: Ahmad Mustapha ; Karine Zeitouni and Yehia Taher

Affiliation: UVSQ, France

Keyword(s): Functional Data Analysis, Database, Spatiotemporal, Multivariate Time Series, Sensors, Opportunistic Mobile Monitoring.

Abstract: The rise of new lightweight and cheap sensors has opened the door wide for new sensing applications. Mobile opportunistic sensing is one type of these applications which has been adopted in multiple citizen science projects including air pollution monitoring. However, the opportunistic nature of sensing along with campaigns being mobile and sensors being subjected to noise and missing values leads to asynchronous and unclean data. Analyzing this type of data requires cumbersome and time-consuming preprocessing. In this paper, we introduce a novel framework to treat such type of data by seeing data as functions rather than vectors. The framework introduces a new data representation model along with a high-level query language and an analysis module.

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Paper citation in several formats:
Mustapha, A.; Zeitouni, K. and Taher, Y. (2018). Towards Rich Sensor Data Representation - Functional Data Analysis Framework for Opportunistic Mobile Monitoring. In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-294-3; ISSN 2184-500X, SciTePress, pages 290-295. DOI: 10.5220/0006788502900295

@conference{gistam18,
author={Ahmad Mustapha. and Karine Zeitouni. and Yehia Taher.},
title={Towards Rich Sensor Data Representation - Functional Data Analysis Framework for Opportunistic Mobile Monitoring},
booktitle={Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2018},
pages={290-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006788502900295},
isbn={978-989-758-294-3},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - Towards Rich Sensor Data Representation - Functional Data Analysis Framework for Opportunistic Mobile Monitoring
SN - 978-989-758-294-3
IS - 2184-500X
AU - Mustapha, A.
AU - Zeitouni, K.
AU - Taher, Y.
PY - 2018
SP - 290
EP - 295
DO - 10.5220/0006788502900295
PB - SciTePress

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